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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.make_blobs</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-blobs">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_blobs</span></code></a></li>
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  <div class="section" id="sklearn-datasets-make-blobs">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>.make_blobs<a class="headerlink" href="#sklearn-datasets-make-blobs" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.make_blobs">
<code class="sig-prename descclassname">sklearn.datasets.</code><code class="sig-name descname">make_blobs</code><span class="sig-paren">(</span><em class="sig-param">n_samples=100</em>, <em class="sig-param">n_features=2</em>, <em class="sig-param">centers=None</em>, <em class="sig-param">cluster_std=1.0</em>, <em class="sig-param">center_box=(-10.0</em>, <em class="sig-param">10.0)</em>, <em class="sig-param">shuffle=True</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/datasets/_samples_generator.py#L704"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.make_blobs" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate isotropic Gaussian blobs for clustering.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>n_samples</strong><span class="classifier">int or array-like, optional (default=100)</span></dt><dd><p>If int, it is the total number of points equally divided among
clusters.
If array-like, each element of the sequence indicates
the number of samples per cluster.</p>
</dd>
<dt><strong>n_features</strong><span class="classifier">int, optional (default=2)</span></dt><dd><p>The number of features for each sample.</p>
</dd>
<dt><strong>centers</strong><span class="classifier">int or array of shape [n_centers, n_features], optional</span></dt><dd><p>(default=None)
The number of centers to generate, or the fixed center locations.
If n_samples is an int and centers is None, 3 centers are generated.
If n_samples is array-like, centers must be
either None or an array of length equal to the length of n_samples.</p>
</dd>
<dt><strong>cluster_std</strong><span class="classifier">float or sequence of floats, optional (default=1.0)</span></dt><dd><p>The standard deviation of the clusters.</p>
</dd>
<dt><strong>center_box</strong><span class="classifier">pair of floats (min, max), optional (default=(-10.0, 10.0))</span></dt><dd><p>The bounding box for each cluster center when centers are
generated at random.</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">boolean, optional (default=True)</span></dt><dd><p>Shuffle the samples.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None (default)</span></dt><dd><p>Determines random number generation for dataset creation. Pass an int
for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape [n_samples, n_features]</span></dt><dd><p>The generated samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array of shape [n_samples]</span></dt><dd><p>The integer labels for cluster membership of each sample.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_classification</span></code></a></dt><dd><p>a more intricate variant</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="gp">... </span>                  <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(10, 2)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span>
<span class="go">array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">centers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="gp">... </span>                  <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(10, 2)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span>
<span class="go">array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0])</span>
</pre></div>
</div>
</dd></dl>

<div class="section" id="examples-using-sklearn-datasets-make-blobs">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_blobs</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-blobs" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different anomaly detection algorithms on 2D datasets. Da..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_anomaly_comparison_thumb.png" src="../../_images/sphx_glr_plot_anomaly_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="When performing classification you often want to predict not only the class label, but also the..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_calibration_thumb.png" src="../../_images/sphx_glr_plot_calibration_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/calibration/plot_calibration.html#sphx-glr-auto-examples-calibration-plot-calibration-py"><span class="std std-ref">Probability calibration of classifiers</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class ..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_calibration_multiclass_thumb.png" src="../../_images/sphx_glr_plot_calibration_multiclass_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/calibration/plot_calibration_multiclass.html#sphx-glr-auto-examples-calibration-plot-calibration-multiclass-py"><span class="std std-ref">Probability Calibration for 3-class classification</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Shows how shrinkage improves classification. "><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_lda_thumb.png" src="../../_images/sphx_glr_plot_lda_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py"><span class="std std-ref">Normal and Shrinkage Linear Discriminant Analysis for classification</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Reference:"><div class="figure align-default" id="id5">
<img alt="../../_images/sphx_glr_plot_mean_shift_thumb.png" src="../../_images/sphx_glr_plot_mean_shift_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py"><span class="std std-ref">A demo of the mean-shift clustering algorithm</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example is meant to illustrate situations where k-means will produce unintuitive and possi..."><div class="figure align-default" id="id6">
<img alt="../../_images/sphx_glr_plot_kmeans_assumptions_thumb.png" src="../../_images/sphx_glr_plot_kmeans_assumptions_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py"><span class="std std-ref">Demonstration of k-means assumptions</span></a></span><a class="headerlink" href="#id6" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Reference: Brendan J. Frey and Delbert Dueck, &quot;Clustering by Passing Messages Between Data Poin..."><div class="figure align-default" id="id7">
<img alt="../../_images/sphx_glr_plot_affinity_propagation_thumb.png" src="../../_images/sphx_glr_plot_affinity_propagation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_affinity_propagation.html#sphx-glr-auto-examples-cluster-plot-affinity-propagation-py"><span class="std std-ref">Demo of affinity propagation clustering algorithm</span></a></span><a class="headerlink" href="#id7" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Finds core samples of high density and expands clusters from them."><div class="figure align-default" id="id8">
<img alt="../../_images/sphx_glr_plot_dbscan_thumb.png" src="../../_images/sphx_glr_plot_dbscan_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py"><span class="std std-ref">Demo of DBSCAN clustering algorithm</span></a></span><a class="headerlink" href="#id8" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Clustering can be expensive, especially when our dataset contains millions of datapoints. Many ..."><div class="figure align-default" id="id9">
<img alt="../../_images/sphx_glr_plot_inductive_clustering_thumb.png" src="../../_images/sphx_glr_plot_inductive_clustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_inductive_clustering.html#sphx-glr-auto-examples-cluster-plot-inductive-clustering-py"><span class="std std-ref">Inductive Clustering</span></a></span><a class="headerlink" href="#id9" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares the timing of Birch (with and without the global clustering step) and Min..."><div class="figure align-default" id="id10">
<img alt="../../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" src="../../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py"><span class="std std-ref">Compare BIRCH and MiniBatchKMeans</span></a></span><a class="headerlink" href="#id10" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is fa..."><div class="figure align-default" id="id11">
<img alt="../../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png" src="../../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py"><span class="std std-ref">Comparison of the K-Means and MiniBatchKMeans clustering algorithms</span></a></span><a class="headerlink" href="#id11" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different linkage methods for hierarchical clustering on ..."><div class="figure align-default" id="id12">
<img alt="../../_images/sphx_glr_plot_linkage_comparison_thumb.png" src="../../_images/sphx_glr_plot_linkage_comparison_thumb.png" />
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</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Silhouette analysis can be used to study the separation distance between the resulting clusters..."><div class="figure align-default" id="id13">
<img alt="../../_images/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png" src="../../_images/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png" />
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</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are &quot;int..."><div class="figure align-default" id="id14">
<img alt="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" src="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></span><a class="headerlink" href="#id14" title="Permalink to this image">¶</a></p>
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</div><div class="sphx-glr-thumbcontainer" tooltip="Plot several randomly generated 2D classification datasets. This example illustrates the datase..."><div class="figure align-default" id="id15">
<img alt="../../_images/sphx_glr_plot_random_dataset_thumb.png" src="../../_images/sphx_glr_plot_random_dataset_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/datasets/plot_random_dataset.html#sphx-glr-auto-examples-datasets-plot-random-dataset-py"><span class="std std-ref">Plot randomly generated classification dataset</span></a></span><a class="headerlink" href="#id15" title="Permalink to this image">¶</a></p>
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</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the maximum margin separating hyperplane within a two-class separable dataset using a line..."><div class="figure align-default" id="id16">
<img alt="../../_images/sphx_glr_plot_sgd_separating_hyperplane_thumb.png" src="../../_images/sphx_glr_plot_sgd_separating_hyperplane_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py"><span class="std std-ref">SGD: Maximum margin separating hyperplane</span></a></span><a class="headerlink" href="#id16" title="Permalink to this image">¶</a></p>
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</div><div class="sphx-glr-thumbcontainer" tooltip="Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corre..."><div class="figure align-default" id="id17">
<img alt="../../_images/sphx_glr_plot_logistic_multinomial_thumb.png" src="../../_images/sphx_glr_plot_logistic_multinomial_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py"><span class="std std-ref">Plot multinomial and One-vs-Rest Logistic Regression</span></a></span><a class="headerlink" href="#id17" title="Permalink to this image">¶</a></p>
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</div><div class="sphx-glr-thumbcontainer" tooltip="This example presents the different strategies implemented in KBinsDiscretizer:"><div class="figure align-default" id="id18">
<img alt="../../_images/sphx_glr_plot_discretization_strategies_thumb.png" src="../../_images/sphx_glr_plot_discretization_strategies_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/preprocessing/plot_discretization_strategies.html#sphx-glr-auto-examples-preprocessing-plot-discretization-strategies-py"><span class="std std-ref">Demonstrating the different strategies of KBinsDiscretizer</span></a></span><a class="headerlink" href="#id18" title="Permalink to this image">¶</a></p>
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</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the maximum margin separating hyperplane within a two-class separable dataset using a Supp..."><div class="figure align-default" id="id19">
<img alt="../../_images/sphx_glr_plot_separating_hyperplane_thumb.png" src="../../_images/sphx_glr_plot_separating_hyperplane_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/svm/plot_separating_hyperplane.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-py"><span class="std std-ref">SVM: Maximum margin separating hyperplane</span></a></span><a class="headerlink" href="#id19" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide the support vecto..."><div class="figure align-default" id="id20">
<img alt="../../_images/sphx_glr_plot_linearsvc_support_vectors_thumb.png" src="../../_images/sphx_glr_plot_linearsvc_support_vectors_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/svm/plot_linearsvc_support_vectors.html#sphx-glr-auto-examples-svm-plot-linearsvc-support-vectors-py"><span class="std std-ref">Plot the support vectors in LinearSVC</span></a></span><a class="headerlink" href="#id20" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The two plots differ only in the area in the middle where the classes are tied. If break_ties=F..."><div class="figure align-default" id="id21">
<img alt="../../_images/sphx_glr_plot_svm_tie_breaking_thumb.png" src="../../_images/sphx_glr_plot_svm_tie_breaking_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/svm/plot_svm_tie_breaking.html#sphx-glr-auto-examples-svm-plot-svm-tie-breaking-py"><span class="std std-ref">SVM Tie Breaking Example</span></a></span><a class="headerlink" href="#id21" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Find the optimal separating hyperplane using an SVC for classes that are unbalanced."><div class="figure align-default" id="id22">
<img alt="../../_images/sphx_glr_plot_separating_hyperplane_unbalanced_thumb.png" src="../../_images/sphx_glr_plot_separating_hyperplane_unbalanced_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/svm/plot_separating_hyperplane_unbalanced.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-unbalanced-py"><span class="std std-ref">SVM: Separating hyperplane for unbalanced classes</span></a></span><a class="headerlink" href="#id22" title="Permalink to this image">¶</a></p>
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